Method for querying long-form speech

ABSTRACT

A method including parsing a query into a tree of operations, generating a query matrix and a transcript matrix, determining a cross-correlation of the query matrix and the transcript matrix, applying a softness map to the cross-correlation to determine one or more matches for each of the literals, and combining the one or more matches utilizing the tree of operations to generate an output, each of the operators corresponding to a combination operation for the matches.

BACKGROUND

Search query engines may be utilized to determine whether words orphrases were used in a text document. Conventional search query enginesfocus on the actual word or phrase that was used instead of the meaningof that word or phrase. Also, those conventional search engines areneither accurate nor efficient. Thus, they may be of limited use inreal-time search query applications, or even overall. Additionally,conventional search query engines do not search speech transcripts thatare enriched with emotional metadata for concepts.

BRIEF SUMMARY

The search query engine converts a search query into a tree ofoperations using literals and operators. The query and a transcript maythen be converted into a matrix of word embeddings that represent themeaning of the word and the cross-correlation of the two matrices iscomputed to find matches. In some instances, the cross-correlation oflarge transcript matrices may be accelerated by utilizing the Fouriertransform of the matrix. Matches are then those dot products that fallwith a softness threshold as determined by a softness map. In additionto matching words, non-speech data (e.g., emotions or speaker role) maybe matched by expanding the dimensions of the word embedding matrices toinclude a metric for various parts of non-speech data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 illustrates an embodiment of a communication system 100.

FIG. 2 illustrates an embodiment of a query method 200.

FIG. 3 illustrates an embodiment of a tree of operations generationmethod 300.

FIG. 4 illustrates an embodiment of a query types 400.

FIG. 5 illustrates an embodiment of a query tree 500.

FIG. 6 illustrates an embodiment of a word embedding method 600.

FIG. 7 illustrates an embodiment of a fast Fourier transformation system700.

FIG. 8 illustrates an embodiment of a fast Fourier transformation method800.

FIG. 9 illustrates an embodiment of a match combination method 900.

FIG. 10 illustrates an embodiment of a communication system 1000.

FIG. 11 illustrates an embodiment of a query method 1100.

FIG. 12 illustrates in an embodiment of a sparse quantitative thesaurusmatrix generation method 1200.

FIG. 13 illustrates a thesaurus 1300 in accordance with one embodiment.

FIG. 14 is an example block diagram of a computing device 1400 that mayincorporate embodiments of the present invention.

DETAILED DESCRIPTION

Disclosed herein are embodiments of unconventional search enginealgorithms that may be executed by a data processing device to returnresults much faster from unstructured or lightly structured data sourcessuch as data files that are machine-generated speech-to-text transcriptsof multi-participant voice conferences. In particular the new algorithmsutilize a combination of processing that's particularly efficient forexecution on text-to-speech converted transcript files, using theinstruction set architecture of modern data processing integratedcircuits such as central processing units (CPUs) and graphics processingunits (CPUs).

Referring to FIG. 1 , a communication system 100 comprises a firstperson 102, a second person 104, a network 106, an audio transformationsystem 108, a speech to text converter 110, an analog to digitalconverter 112, an enrichment logic 114, a digital transcript 116, athird person 118, a query engine 120, a query parser 122, a matrixgenerator 124, a query word embedding matrix 126, a transcript wordembedding matrix 128, a cross-correlator 130, a comparator 132, asoftness map 134, and a combiner 136.

The first person 102 is in audio communication with a second person 104over a network 106, for example an IP network, analog telephone network,or cellular network.

Audio from the communications may be recorded, or streamed live to anaudio transformation system 108, which converts the audio tometadata-enriched text. The audio transformation system 108 may comprisea speech to text converter 110 and enrichment logic 114 to transform theaudio into the enriched text. If the audio is in an analog format, theaudio transformation system 108 may utilize an analog to digitalconverter 112 to convert to a digital format before providing thedigital audio to the speech to text converter 110.

The enriched text of the audio is output in the form of one or moredigital files of a digital transcript 116. A third person 118 may searchthe digital transcript 116 using queries. The queries, along with thedigital transcript 116, are operated on by a query engine 120. The queryengine 120 may be operated according to the process depicted in FIG. 2 .

The query engine 120 inputs the query to a query parser 122 to generatea tree of operations from words (literals) and operators of the query.The query parser 122 may generate the tree of operation in accordancewith the process depicted in FIG. 3 . The query parser 122 may furtherutilize the query types 400 to parse the query into the tree ofoperations. The literals and the digital transcript 116 are input to amatrix generator 124 to generate a query word embedding matrix 126 and atranscript word embedding matrix 128. The matrices may be generated inaccordance with the process depicted in FIG. 6 . These two matrices areinput to a cross-correlator 130 to generate dot product pairs, which areinput to a comparator 132. In some embodiments, such as those with largematrices, a fast Fourier transformation system may be utilized togenerate the dot products. An embodiment of this system is depicted inFIG. 7 . The comparator 132 identifies matches from dot products thatfall with a softness threshold as determined by a softness map 134. Thematches are combined (combiner 136) based on the operators extractedfrom the query by the query parser 122. The combiner 136 may be operatedin accordance with the process depicted in FIG. 9 . The combiner 136generates an output. The combiner 136 may limit the number of outputs toa number of highest results, all results, or no results if the finalweight is too low.

Referring to FIG. 2 , a query method 200 receives a transcript (block202). The transcript may be the digital transcript 116 discussed in FIG.1 . A query is then received (block 204). The query may comprise thequery types discussed in reference to FIG. 4 . The query is thentransformed into a tree of operations comprising literals and operators(block 206). The tree of operation comprises operators as the stems andliterals as the leaves. The operators may be unary or binary, that is,one or two connections, respectively, to a lower level on the tree ofoperations. The tree of operations may be generated by the processdepicted in FIG. 3 . Other tree generating algorithms may be utilized.The literals and the transcript are each transformed into matrix of wordembeddings (block 208). The word embeddings may be stored in a controlmemory structure. The word embeddings may be multi-dimensional, such as50-1000 dimensions. 300-dimension word embedding may be utilized tooptimize efficiency and accuracy. The word embedding may be generated inaccordance with the process depicted in FIG. 6 . The dimension ofembeddings may be expanded to store other non-speech information. Forexample, the speaker role (i.e., “agent” vs “caller”) or the emotionalcontent (0.0-1.0 based on how angry the speaker was) may be included.For “soft” outputs like the emotion model above, so long as it retains ahigher match in a dot product (i.e., 1 dotted with 0.9 will be a highmatch), the dimension of the transcript embeddings may be extended toinclude the model outputs and the query embedding can be extended toinclude the query flag (0 vs 1 or similar). For any exemplary vectorwith 300-dimension word embeddings, an additional “301st” or moredimension is included to represent the non-speech information. For hardmetadata (i.e., speaker role), this same method may be utilized, or asearch index may be utilized to filter down transcript segments thatmatch that metadata flag. Each literal may have its own query matrix.The cross-correlation of transcript and query are computed (block 210).The cross-correlation may be determined by:C _(n)=Σ_(i=0) ^(l) T _(i) ·Q _(i+n)   Equation 1

where C is the cross-correlation, T is the transcript matrix, Q is thequery matrix, and l is the length of the transcript matrix, which isdetermined based on the number of words in the transcript. In someembodiments, such as larger transcript matrices, the cross-correlationis determined utilizing the Fourier transform of the matrices and theconvolution Theorem. An exemplary system is depicted in FIG. 7 and theprocess is depicted in FIG. 8 . A threshold matrix size may be utilizedto determine whether the fast Fourier transformation system 700 isutilized. A cross-correlation may be determined for each literal. Thecross-correlation is then compared to a softness map to determinematches (block 212). The softness map may be based on the degree ofsoftness for the given literal(s). The softness map returns thresholdsfor each literal as each literal may have a different softness. Thecross-correlation is compared to the threshold from the softness matrixto determine matches. Those cross-correlations for each literal that areexceed the threshold are determined to be matches. The matches andoperators are utilized to execute the tree of operations to return theoutput (block 214). This may be performed in accordance with the processdepicted in FIG. 9 . The query method 200 may also utilize ashunting-yard algorithm to determine the output. Each operator may havecomposition rules stored to determine the effect on the matches. Thematches for each literal may replace the literal in the tree ofoperations and multiple permutations of the tree of operations performedif multiple matches are determined for a literal. Prior to being output,a further threshold may be performed to eliminate those outputs with alow weight. The output may comprise the location of the match, theweight of the match, the query, the match, any extractions, etc.

In some embodiments, a query may be performed on a phrase. While thecross correlation behaves well on longer phrases, word ordering affectsmeaning. As such, being out of order may be penalized while permittingsome word reordering. One method is to convolve the transcript embeddingmatrix with a kernel (e.g., a Gaussian kernel) in a soft query. Thisblurs the location of words by a few places, allowing word reordering tobe tolerated to some degree. The convolution may also be performed onthe query embedding matrix. This is functionally the same as thecross-correlation and may be determined by:B _(n)=Σ_(i=0) ^(l) C _(i) ·K _(i+n)  Equation 2

where B is the resulting blurred matrix, C is the matrix to be blurred,K is the Kernel, and l is the length of the matrix to be blurred. Anexample Kernel is: K=[0.05, 0.1, 0.7, 0.1, 0.05].

Referring to FIG. 3 , a tree of operations generation method 300receives a query (block 302). The presence of compound queryindicator(s) is determined (block 304). If the tree of operationsgeneration method 300 determines (decision block 306) that an indicatoris not present, the tree of operations generation method 300 determinewhether an operator is present (decision block 308). If not, the literalis determined (block 310). Any modifiers to the literal, such as thesoftness are also determined. If an operator is determined to be presentthat operator is determine (block 312). The operator is then sent toblock 318.

If a compound query indicator is determined to be present, the innermostindicator is initialized (block 314). The indicator may be a set ofparentheses. Mathematical operations may be utilized to determine whichindicator is the innermost. If two indicators may both be consideredinnermost, one is selected. One such scheme is to select the indicatorthat is first from left to right. The innermost operator is thendetermined and set as the current operator (block 316). A counter is setto “1” (block 318). The counter may generally be initialized to anynumber or other value in other embodiments. The current operator isplaced at level “counter+1” (block 320). The literal(s) are determinedfor the current operator (block 322). Those literals are placed at level“counter” and connected to current operator (block 324). The tree ofoperations generation method 300 then determines whether there isanother indicator or operator (decision block 326). If so, the currentoperator is stored as a “literal” for the next connected operator at ahigher level (block 328). The next indicator is determined (block 330).In cases where another operated is detected but no indicator isdetermined, the tree of operations generation method 300 may treat thatoperator as being in an indicator. The counter is incremented if thenext indicator is at a higher level (block 332). The next operator isdetermined (block 334). The next operator is set as the current operator(block 336). The tree of operations generation method 300 then beginsfrom block 320. Once only a literal is determined or there are noadditional operators or indicators, the tree of operations generationmethod 300 ends (done block 338).

Referring to FIG. 4 , query types 400 that may be stored in a querycontrol memory structure 402 are depicted. The query types 400 maycomprise literals 404, phrase operators 406, conversation operators 408,segment modifiers 410, compound queries 412, extractors 414, timeoperators 416, and metadata 418. The above does not constitute anexhaustive list of the query types 400.

The literals 404 are extracted from a query and compared to thetranscript. The literals 404 may be indicated by quotations around aword or phrase. For example, the literals 404 may be “crash”, “lostcredit card”, etc. Single quotes may be utilized as well in someembodiments, such as ‘crash’. In other embodiments, other indicators forthe literals 404 may be utilized. The indicators are utilized todetermine which text is to be compared to the transcript. The literals404 have an associated softness. The literals 404 may have a defaultsoftness of 0. However, this softness may be increased by a softnessindicator, such as one to more tildes (˜) added before the quoted wordor phrase to “loosen up” similar matches (semantically, meaning similarin meaning not sound). In one embodiment, one tilde matches similarforms like plurals or conjugates. For example, ˜“crash” matches“crashes” or “crashing”. Two tildes match synonymous words. For example,˜˜“crash” matches “accident” or “collision”. Three tildes match relatedphrasings. For example, ˜˜˜“have a nice day” matches “i hope your day isgreat”. The softness associated with the literals 404 may be utilized todetermine a threshold value for potential matches and incorporated intoa softness map.

The phrase operators 406 are utilized to search within a speech segmentfor two things (e.g., the literals 404). Exemplary phrase operators 406include “near”, “or”, or “then”. For example, a query for ˜˜“crash” near“honda”, looks for both ˜˜“crash” and “honda”. The query ˜˜“crash” or“ticket” looks for either ˜˜“crash” or “ticket” or both. The query˜˜“crash” then “police report” looks for both ˜˜“crash” and “policereport” in order. That is, a transcript, “I had an accident and thenthey wrote a police report”, would match; however, the transcript, “Ifound the police report after the crash”, would not. The phraseoperators 406 are placed within a tree of operations and utilized tocombine the matches of the literals 404, if any.

The conversation operators 408 are utilized to search across an entireconversation for two things. Exemplary conversation operators 408include “and”, “or”, and “later”. The “and” operator looks for aconversation that contains both literals. They query ˜˜“lost card” and“two weeks” may match a conversation that looks like this:

-   -   Hello thanks for calling.    -   . . .    -   I want to report a missing card.    -   . . .    -   The new card should arrive in one to two weeks.    -   . . . .

However, by contrast the “near” operator may not match, because theyspan different speech segments. The “or” operator looks for aconversation that contains either literals or both. Its use isdetermined by context relative to the phrase scanner. The query caller˜˜“lost card” or caller “two weeks” may match the followingconversation:

-   -   Hello thanks for calling.    -   . . .    -   I want to report a missing card.    -   . . .    -   The new card should arrive in five days.    -   . . . .

The “later” operator looks for a conversation that contains bothliterals in order. For example, the query ˜˜˜“reset my password” later˜“thanks” may match the following conversation:

-   -   Hello thanks for calling.    -   . . .    -   I need my password reset.    -   . . .    -   Thank you!    -   . . . .

However, if the final “thank you” was omitted, the conversation wouldnot match, even though “thanks” was said earlier in the conversation.

The segment modifiers 410 are additional modifiers that may be placed tothe left of a segment to restrict it to a certain property or modify itin some other way. Exemplary segment modifiers 410 include “agent”,“caller”, and “not”. The “agent” segment modifier applies if an agentsays the following phrase. An example query is agent ˜˜“great to hear”.The “caller” segment modifier applies if a caller says the followingphrase. An example query is caller ˜˜“very helpful”. The “not” segmentmodifier applies if the following phrase does not occur. An exemplaryquery is not ˜˜“claim”. Additionally, the segment modifiers 410 may bestacked (although order can affect meaning), such as not agent ˜˜“sorry”matches a conversation in which an agent does not apologize.

The compound queries 412 are utilized to build more complex queries. Thecompound queries 412 may be indicated by the utilization of parenthesesin one embodiment. Other embodiments may utilize symbols to indicate thecompound queries 412. Inner scanners are evaluated and then combinedwith outer scanners. An example is (˜˜“crash” near ˜˜“police report”) or˜˜˜“file a claim”. This phrase matches if a crash and police report areboth mentioned or if a claim is filed (or both). However, “policereport” alone would not match. The compound queries 412 may be donemultiple times, such as ((((˜˜“crash” near ˜˜“police report”) or˜˜˜“file a claim”) later agent ˜˜“sorry”) and caller not ˜˜“thank you”)or “thank you for your help with the claim”.

The extractors 414 are special phrases that may be indicated by curlybraces “{ }” that represent a concept. In some embodiments, theextractors 414 are treated as if they have two tildes and thus can beomitted. The query ˜˜“hello my name is {name}” may match “hi my name isGeorge”. Further examples with likely matches include{firstName}—Anthony, Steve; {surname}—Richardson, Hernandez;{fullName}—Anthony Richardson, Steve Hernandez; {date}—March Fifth,Christmas; {time}—Five thirty a.m., Noon; {greeting}—Hi there, goodmorning; {polite}—Thanks, please; {positive}—Great, wonderful, amazing;{negative}-Terrible, awful, sad; {company}—Microsoft®, {zipCode}—Nine ohtwo one oh; {title}-Mister, Miss, Doctor; and {phoneNumber}—Eight sixseven five three oh nine.

The time operators 416 place time constraints on scanners. A maximumduration, or less than an amount of time has passed, may be specified byutilizing an indicator, such as square brackets as well as the less thanoperator, a number, and units, such as [<30 s] is less than 30 seconds,[<5 s] is less than five seconds, and [<5 m] is less than five minutes.The query “interest rate” [<30 s] “a. p. r.” looks for the phrase “a. p.r.” less than thirty seconds after “interest rate”. A minimum durationis similar to the maximum duration but requires that there be more thanthe specified amount of time between phrases. Examples include [>20 s]is more than 20 seconds, [>100 s] is more than one hundred seconds, and[>15 m] is more than fifteen minutes. Start and end tokens are timeoperators 416 that may be utilized to specify the start and end of thecall. For example, {start} [<30 s] “thanks for calling” looks for“thanks for calling” being said in the first thirty seconds. Similarly,{end} can indicate the end of the call. The query “anything else today”[>1 m] {end} may enforce that “anything else today” was said greaterthan a minute before the end of the call.

The metadata 418 may be utilized to place constraints on call metadata,such as the date, start time, duration, or user-provided metadata. Themetadata queries may be performed first, and then scanner is performedon the resulting subset.

Referring to FIG. 5 , a query tree 500 comprises a first literal 502, afirst operator 504, a compound query 506, a second literal 508, a secondoperator 510, and a third literal 512. The query tree 500 is generatedfrom the query: ˜˜“lost” and (˜˜“debit” then “card”). The query is thencompared to the transcript: “i think i have misplaced my credit card”.

As the query has compound query indicators, here parentheses, thatportion of the query is operated on first. The second operator 510 isdetermined to be the operator within the compound query 506 and isplaced within the second level of the query tree 500. The literals forthe second operator 510, the second literal 508 and the third literal512, are determined and place in the first level of the query tree 500,connected to the second operator 510. The word or phrase of the literaland the associated softness is determined, which will then be utilizedto compare to the transcript. The next operator, the first operator 504,is then determined and placed in the third level of the query tree 500.The connectors are then determined for the first operator 504, which arethe first literal 502 and the second operator 510. The first literal 502also has its word or phrase and associated softness determined to beutilized to compare to the transcript.

Referring to FIG. 6 , a word embedding method 600 determines a number ofwords for the query or transcript (block 602). The word embedding method600 may be performed on both the query and the transcript. The query ortranscript vector is generated with a length equal to the number ofwords (block 604). The first word is selected (block 606) and set as thecurrent word (block 608). The embedding vector for current word is thendetermined (block 610). The embedding vector may be pre-determined andstored to be retrieved. The embedding vector may be between 50 and 1000dimensions in some embodiments. The embedding vector is placed intoquery or transcript vector (block 612). The embedding vector replacesthe word in the query or transcript vector. The word embedding method600 then determines whether there is another word (decision block 614).If so, the next word is selected (block 616) and the word embeddingmethod 600 is performed from block 608. Once the words are replaced bytheir word embeddings, the word embedding method 600 ends.

Referring to FIG. 7 , a fast Fourier transformation system 700 comprisesa query word embedding matrix 702, a transcript word embedding matrix704, a Fourier fast transformer 706, a cross-correlator 708, and aninverse Fourier fast transformer 710.

The query word embedding matrix 702 and the transcript word embeddingmatrix 704 may be received from a matrix generator. The Fourier fasttransformer 706 performs a Fourier transformation on the query wordembedding matrix 702 and transcript word embedding matrix 704 toaccelerate the performance of the cross-correlator 708 when generatingthe dot products for comparison. The cross-correlator 708 may performpoint-wise multiplication and send the results to the inverse Fourierfast transformer 710. The output of the cross-correlator 708 may then bereverse transformed by the inverse Fourier fast transformer 710 using aninverse Fourier transform. The fast Fourier transformation system 700may be operated in accordance with the process depicted in FIG. 8 .

The fast Fourier transformation system 700 may be the default or analternate system to perform the cross-correlation. A threshold may beutilized, based on factors, such as matrix size, to determine whether toutilize the fast Fourier transformation system 700.

Referring to FIG. 8 , a fast Fourier transformation method 800 receivestranscript and query matrices (block 802). A Fourier transform isapplied on the transcript matrix and the query matrix (block 804). Apoint-wise multiplication is applied between the matrices (block 806).An inverse Fourier transform is applied to the point-wise product of thematrices (block 808). The resulting “dot products” are then sent to acomparator to determine any matches.

Referring to FIG. 9 , a match combination method 900 replaces literalswith matches (block 902). The literals may be received as part of a treeof operation. The matches may be received from a comparator. The numberof levels in tree of operation is determined (block 904). The lowestlevel is selected (block 906). A first pair of matches at the level isselected (block 908). If multiple pairs are at the same level, one maybe selected randomly, or by position (e.g., left-most) to be performedfirst if performed in series. The pairs may be evaluated in parallel. Incases of a unary operator, the literal for that operator is selected. Insome scenarios, the “literal” to be operated on is the result of anoperator acting on a literal(s), such as for a compound query. Theconnecting operator is determined (block 910). The operationcorresponding to the operator is determined (block 912). The operationmay be stored along with the operator and retrieved to be performed onthe literal(s). Exemplary operations include the “and” operatorrequiring a match in both literals. The new start is the minimum of thetwo original literal starts. The new end is the maximum of the two ends.The new match is the original two match strings concatenated with and(i.e., “credit” and “card”). The new query is combined in a similar way.The weight is the product of the input weights. In this way, “and”behaves like the match on the product of two cross-correlations. The“or” operator behaves similarly to “and”, except it produces a sumrather than a product of the weights. The “then” operator behaves like“and” but requires time ordering be enforced. The “not” operator (whichis a unary operator) inverts the input signal and adds small matchregions at the ends so that it works with the “then” operator. Theoperation is applied to the literal, paired or otherwise (block 914).The operator(s) is altered to the result of paired matches (block 916).The tree of operations is reduced, and the position of the operator maynow include the result of the operation performed on the literal(s) orprevious replacement of an operator. In some embodiments, a thresholdvalue is applied after the operation is performed to remove the match asa potential output. The match combination method 900 determines whetherthere is another pair at the level (decision block 918). If so, the nextpair of matches is selected (block 920). As above one literal or reducedoperator is selected for a unary operator. The match combination method900 then is performed on the next pair from block 910.

Once a level has been reduced by operators, the match combination method900 determines if there is another level (decision block 922). If so,the next level is selected (block 924), and the match combination method900 is performed on the next level from block 908. Once all levels havebeen reduced, an output is generated. The output may include the start,end, weight, query, match, and extractions. Other information may beprovided. The output may also be applied to the transcript to, forexample, highlight the output. The match combination method 900 thenends (done block 926).

Referring to FIG. 10 , a communication system 1000 comprises a firstperson 1002, a second person 1004, a network 1006, an audiotransformation system 1008, a speech to text converter 1010, an analogto digital converter 1012, an enrichment logic 1014, a digitaltranscript 1016, a third person 1018, a query engine 1020, a queryparser 1022, a search engine 1024, a quantitative thesaurus matrix 1026,and a combiner 1028.

The first person 1002 is in audio communication with a second person1004 over a network 1006, for example an IP network, analog telephonenetwork, or cellular network.

Audio from the communications may be recorded, or streamed live to anaudio transformation system 1008, which converts the audio tometadata-enriched text. The audio transformation system 1008 maycomprise a speech to text converter 1010 and enrichment logic 1014 totransform the audio into the enriched text. If the audio is in an analogformat, the audio transformation system 1008 may utilize an analog todigital converter 1012 to convert to a digital format before providingthe digital audio to the speech to text converter 1010.

The enriched text of the audio is output in the form of one or moredigital files of a digital transcript 1016. A third person 1018 maysearch the digital transcript 1016 using queries. The queries, alongwith the digital transcript 1016, are operated on by a query engine1020. The query engine 1020 may be operated according to the processdepicted in FIG. 12 .

The query engine 1020 inputs the query to a query parser 1022 togenerate a tree of operations from words (literals) and operators of thequery. The query parser 1022 may generate the tree of operation inaccordance with the process depicted in FIG. 3 . The query parser 1022may further utilize the query types 400 to parse the query into the treeof operations. The literals and the digital transcript 1016 are input toa search engine 1024, which then retrieves the matches from thequantitative thesaurus matrix 1026. The quantitative thesaurus matrix1026 may be generated based on the process depicted in FIG. 12 . Thematches are combined (combiner 1028) based on the operators extractedfrom the query by the query parser 1022. The combiner 1028 may beoperated in accordance with the process depicted in FIG. 9 .

Referring to FIG. 11 , the transcript is received (block 1102). Thequery is also received (block 1104). The query is transformed into atree of operations comprising literals and operators (block 1106). Thisstep may be performed in accordance with the process depicted in FIG. 3. The literals and transcript are transformed into vectors of words(block 1108). The stored value for each query word-transcript word pairis retrieved (block 1110). The value may be stored in a sparse matrix.The sparse matrix may be generated in accordance with the processdepicted in FIG. 12 . Multiple thesauruses may be generated for eachsoftness level. If a large document set is stored in a traditionalsearch index (i.e., a hash-indexed table), the sparse matrix of wordsimilarities may also be utilized to “explode” a query into the similarwords. The exploded queries may also have similar composition rules foroperators. This enables an approximate version of the scanner algorithmto be run as a pre-process against a traditional search index. Forexample, if the query is ˜˜“lost” it may be exploded to a hard query of“lost”, “misplaced”, “missing”, etc. against a traditional search index.For single-word queries, this is exact. For phrase matches, this isapproximate, but by setting the thresholds correctly, this may be aclose approximation. The retrieved values are set as matches (block1112). The matches and operators are utilized to execute the tree ofoperations and return output (block 1114). This may be performed inaccordance with the process depicted in FIG. 9 .

In another embodiment, the query method 1100 is utilized to pre-processa transcript comprising multiple documents. The search may be utilizedto reduce the number of documents to perform the full scanner matrixoperation to a small set of very relevant documents. That is, thetranscript may initially include multiple documents. The query method1100 is applied and those documents with the similar words are kept inthe transcript to perform the full scanner operation, such as theprocess depicted in FIG. 2 .

Referring to FIG. 12 , a dot product is performed between two wordvectors (block 1202). thresholding softness is performed on the dotproduct (block 1204). The result is stored in a sparse matrix (block1206). An exemplary thesaurus is depicted in FIG. 13 .

Referring to FIG. 13 , a thesaurus 1300 comprises similarity scores 1302for the words aardvark, lost, misplaced, and zebra. The similarityscores 1302 may be determined by the process depicted in FIG. 12 . Whena query is received with one of the depicted words, the thesaurus 1300may be searched. The similarity score may then be utilized along withthe similar word(s) to construct another search(es). The similar word(s)may also be utilized to reduce a set of documents with those words. Forexample, if lost was the query word, misplaced may be selected as asimilar word as the similarity score is 0.9. However, aardvark and zebramay not be selected as the similarity score is 0.1.

FIG. 14 is an example block diagram of a computing device 1400 (orcomputing apparatus) that may incorporate embodiments of the presentinvention. FIG. 14 is merely illustrative of a machine system to carryout aspects of the technical processes described herein, and does notlimit the scope of the claims. One of ordinary skill in the art wouldrecognize other variations, modifications, and alternatives. In oneembodiment, the computing device 1400 typically includes a monitor orgraphical user interface 1402, a data processing system 1420, acommunication network interface 1412, input device(s) 1408, outputdevice(s) 1406, and the like.

As depicted in FIG. 14 , the data processing system 1420 may include oneor more processor(s) 1404 that communicate with a number of peripheraldevices via a bus subsystem 1418. These peripheral devices may includeinput device(s) 1408, output device(s) 1406, communication networkinterface 1412, and a storage subsystem, such as a volatile memory 1410and a nonvolatile memory 1414.

The volatile memory 1410 and/or the nonvolatile memory 1414 may storecomputer-executable instructions and thus forming logic 1422 that whenapplied to and executed by the processor(s) 1404 implement embodimentsof the processes disclosed herein.

The input device(s) 1408 include devices and mechanisms for inputtinginformation to the data processing system 1420. These may include akeyboard, a keypad, a touch screen incorporated into the monitor orgraphical user interface 1402, audio input devices such as voicerecognition systems, microphones, and other types of input devices. Invarious embodiments, the input device(s) 1408 may be embodied as acomputer mouse, a trackball, a track pad, a joystick, wireless remote,drawing tablet, voice command system, eye tracking system, and the like.The input device(s) 1408 typically allow a user to select objects,icons, control areas, text and the like that appear on the monitor orgraphical user interface 1402 via a command such as a click of a buttonor the like.

The output device(s) 1406 include devices and mechanisms for outputtinginformation from the data processing system 1420. These may include themonitor or graphical user interface 1402, speakers, printers, infraredLEDs, and so on as well understood in the art.

The communication network interface 1412 provides an interface tocommunication networks (e.g., communication network 1416) and devicesexternal to the data processing system 1420. The communication networkinterface 1412 may serve as an interface for receiving data from andtransmitting data to other systems. Embodiments of the communicationnetwork interface 1412 may include an Ethernet interface, a modem(telephone, satellite, cable, ISDN), (asynchronous) digital subscriberline (DSL), FireWire, USB, a wireless communication interface such asBlueTooth or WiFi, a near field communication wireless interface, acellular interface, and the like.

The communication network interface 1412 may be coupled to thecommunication network 1416 via an antenna, a cable, or the like. In someembodiments, the communication network interface 1412 may be physicallyintegrated on a circuit board of the data processing system 1420, or insome cases may be implemented in software or firmware, such as “softmodems”, or the like.

The computing device 1400 may include logic that enables communicationsover a network using protocols such as HTTP, TCP/IP, RTP/RTSP, IPX, UDPand the like.

The volatile memory 1410 and the nonvolatile memory 1414 are examples oftangible media configured to store computer readable data andinstructions to implement various embodiments of the processes describedherein. Other types of tangible media include removable memory (e.g.,pluggable USB memory devices, mobile device SIM cards), optical storagemedia such as CD-ROMS, DVDs, semiconductor memories such as flashmemories, non-transitory read-only-memories (ROMS), battery-backedvolatile memories, networked storage devices, and the like. The volatilememory 1410 and the nonvolatile memory 1414 may be configured to storethe basic programming and data constructs that provide the functionalityof the disclosed processes and other embodiments thereof that fallwithin the scope of the present invention.

Logic 1422 that implements embodiments of the present invention may bestored in the volatile memory 1410 and/or the nonvolatile memory 1414.Said logic 1422 may be read from the volatile memory 1410 and/ornonvolatile memory 1414 and executed by the processor(s) 1404. Thevolatile memory 1410 and the nonvolatile memory 1414 may also provide arepository for storing data used by the logic 1422.

The volatile memory 1410 and the nonvolatile memory 1414 may include anumber of memories including a main random access memory (RAM) forstorage of instructions and data during program execution and a readonly memory (ROM) in which read-only non-transitory instructions arestored. The volatile memory 1410 and the nonvolatile memory 1414 mayinclude a file storage subsystem providing persistent (non-volatile)storage for program and data files. The volatile memory 1410 and thenonvolatile memory 1414 may include removable storage systems, such asremovable flash memory.

The bus subsystem 1418 provides a mechanism for enabling the variouscomponents and subsystems of data processing system 1420 communicatewith each other as intended. Although the communication networkinterface 1412 is depicted schematically as a single bus, someembodiments of the bus subsystem 1418 may utilize multiple distinctbusses.

It will be readily apparent to one of ordinary skill in the art that thecomputing device 1400 may be a device such as a smartphone, a desktopcomputer, a laptop computer, a rack-mounted computer system, a computerserver, or a tablet computer device. As commonly known in the art, thecomputing device 1400 may be implemented as a collection of multiplenetworked computing devices. Further, the computing device 1400 willtypically include operating system logic (not illustrated) the types andnature of which are well known in the art.

Terms used herein should be accorded their ordinary meaning in therelevant arts, or the meaning indicated by their use in context, but ifan express definition is provided, that meaning controls.

“Circuitry” in this context refers to electrical circuitry having atleast one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, circuitry forming a generalpurpose computing device configured by a computer program (e.g., ageneral purpose computer configured by a computer program which at leastpartially carries out processes or devices described herein, or amicroprocessor configured by a computer program which at least partiallycarries out processes or devices described herein), circuitry forming amemory device (e.g., forms of random access memory), or circuitryforming a communications device (e.g., a modem, communications switch,or optical-electrical equipment).

“Firmware” in this context refers to software logic embodied asprocessor-executable instructions stored in read-only memories or media.

“Hardware” in this context refers to logic embodied as analog or digitalcircuitry.

“Logic” in this context refers to machine memory circuits,non-transitory machine readable media, and/or circuitry which by way ofits material and/or material-energy configuration comprises controland/or procedural signals, and/or settings and values (such asresistance, impedance, capacitance, inductance, current/voltage ratings,etc.), that may be applied to influence the operation of a device.Magnetic media, electronic circuits, electrical and optical memory (bothvolatile and nonvolatile), and firmware are examples of logic. Logicspecifically excludes pure signals or software per se (however does notexclude machine memories comprising software and thereby formingconfigurations of matter).

“Software” in this context refers to logic implemented asprocessor-executable instructions in a machine memory (e.g. read/writevolatile or nonvolatile memory or media).

“quantitative thesaurus matrix” in this context refers to a matrix ofsimilarity scores with indexes of query word-transcript word pairs.

“tree of operations” in this context refers to a structure depicting theorder of operations of operators on the literals and the matches to theliterals.

“transcript word embedding matrix” in this context refers to atranscript matrix that had each word transformed into a N-dimensionalrepresentation (word embedding). For N=300 and the transcript “Hi myname is Al”, the transcript word embedding matrix is a 5×300 matrix.

“query word embedding matrix” in this context refers to a query matrixthat had each word transformed into a N-dimensional representation (wordembedding). For N=300 and the query “today is beautiful”, the query wordembedding matrix is a 3×300 matrix.

“query” in this context refers to a string of symbols that includes atleast one literal and may include multiple literals and operators. E.g.,“lost” then “card” includes two literals, lost and card, as well as theoperator, then.

“literal” in this context refers to a word or phrase. E.g., “card”.

“query word-transcript word pair” in this context refers to a pair ofwords determined by combining one word from the query matrix and oneword from the transcript matrix. E.g., for the query “lost” and thetranscript “I misplaced my card”, there are four pairs, [lost, I],[lost, misplaced], [lost, my], and [lost, card].

“Word embedding” in this context refers to a learned representation fortext where words that have the same meaning have a similarrepresentation in a compact vector space. A benefit of the denserepresentations is generalization power: if certain features of howwords are used in context provide clues, to their similar meaning, theword embedding representation may reflect these similarities. Wordembeddings are a class of techniques where individual words arerepresented as real-valued vectors in a predefined vector space. Eachword is mapped to one vector and the vector values can be learned, forexample using a neural network. Each word is represented by areal-valued vector, often tens or hundreds of dimensions. This iscontrasted to the thousands or millions of dimensions required forsparse word representations, such as a one-hot encoding. Each word inthe vocabulary is represented by a feature vector that encodes differentaspects of the word. Thus, each word is associated with a point in avector space. The number of features (and hence the dimensionality ofthe vector) is much smaller than the size of the vocabulary. Thedistributed vector representation is learned based on the usage ofwords. This allows words that are used in similar ways to result inhaving similar vector representations, naturally capturing theirmeaning. This can be contrasted with the crisp but fragilerepresentation in a bag of words model where, unless explicitly managed,different words have different representations, regardless of how theyare used. The underlying linguistic theory is that words that havesimilar context will have similar meanings. “You shall know a word bythe company it keeps.”

“softness” in this context refers to a degree of relatedness betweenwords. E.g., a softness of 2 may correspond to a synonym.

“query matrix” in this context refers to a vector with a lengthcorresponding to the number of words in a literal and comprising theliteral. The query matrix for the query “card” is a 1×1 matrix of[card]. The query matrix for the query “today is beautiful” is a 3×1matrix: [today, is, beautiful].

“query flag” in this context refers to an indicator that a particularnon-speech information is to be utilized for a word in a query. E.g., a“1” may indicate utilization and a “0” non-utilization.

“matches” in this context refers to a cross-correlation that exceeds asoftness map.

“softness map” in this context refers to a threshold value correspondingto a given softness. E.g., a softness 1 may correspond to a softness mapof 0.95.

“non-speech information” in this context refers to information regardingthe meaning of a word, such as emotion, the speaker, etc. that is notthe word itself.

“cross-correlation” in this context refers to a measure of similarity oftwo series as a function of the displacement of one relative to theother.

“transcript matrix” in this context refers to a vector with a lengthcorresponding to the number of words in a transcript and comprising thewords of the transcript. The transcript matrix for the transcript “Hi,my name is Al” is a 5×1 matrix of [Hi, my, name, is, Al].

“operator” in this context refers to a symbolic representation of anoperation to be performed on one or two literals. E.g., and, then, or,etc.

“similarity score” in this context refers to a measure of the similaritybetween two word for a softness value. The similarity score for twowords may be determined by the cross-correlation of the N-dimensionalword vectors of the two words.

Herein, references to “one embodiment” or “an embodiment” do notnecessarily refer to the same embodiment, although they may. Unless thecontext clearly requires otherwise, throughout the description and theclaims, the words “comprise,” “comprising,” and the like are to beconstrued in an inclusive sense as opposed to an exclusive or exhaustivesense; that is to say, in the sense of “including, but not limited to.”Words using the singular or plural number also include the plural orsingular number respectively, unless expressly limited to a single oneor multiple ones. Additionally, the words “herein,” “above,” “below” andwords of similar import, when used in this application, refer to thisapplication as a whole and not to any particular portions of thisapplication. When the claims use the word “or” in reference to a list oftwo or more items, that word covers all of the following interpretationsof the word: any of the items in the list, all of the items in the listand any combination of the items in the list, unless expressly limitedto one or the other. Any terms not expressly defined herein have theirconventional meaning as commonly understood by those having skill in therelevant art(s).

Various logic functional operations described herein may be implementedin logic that is referred to using a noun or noun phrase reflecting saidoperation or function. For example, an association operation may becarried out by an “associator” or “correlator”. Likewise, switching maybe carried out by a “switch”, selection by a “selector”, and so on.

What is claimed is:
 1. A method of reducing computational overhead of asearch query, the method comprising: parsing the search engine queryinto literals and operators, wherein each of the literals comprises atleast one word and wherein each of the operators is one of a binaryoperator and a unary operator, and the operators include at least one ofphrase operators, conversation operators, and time operators; generatinga query matrix from the literals; generating a transcript matrix from adocument of a transcript data source, wherein the transcript data sourcecomprises multiple documents storing metadata-enriched text generatedfrom recorded or streamed audio communications, wherein themetadata-enriched text includes word embeddings and embedded non-speechdata, wherein the word embeddings represent individual words usingreal-valued vectors in a predefined vector space and wherein words thathave a same meaning have a similarity score indicating related vectorreresentations in the predefined vector space, and wherein thesimilarity score is determined by the cross-correlation of thereal-valued vectors representing the words in the predefined vectorspace, wherein the embedded non-speech data stores non-speechinformation detected in the recorded or streamed audio communications,and wherein the non-speech information includes at least one of:information regarding the meaning of words in the metadata-enrichedtext, a speaker role of a speaker in the recorded or streamed audiocommunications, and an emotion detected for the speaker from therecorded or streamed audio communication; determining across-correlation of the query matrix and the metadata-enriched text inthe transcript matrix; applying a softness map to the cross-correlationto determine one or more matches for each of the literals, the softnessmap generated from a thresholding softness assigned to each of theliterals as part of the search engine query, wherein the thresholdingsoftness assigned to each of the literals determines how much semanticdifference is permitted between the literal and the one or more matchesfor that literal, and wherein the one or more matches are identifiedfrom the cross-correlations that fall above the thresholding softnessassigned to the literal; building a tree of operations, the tree ofoperations comprising the literals and the operators of the searchengine query; combining the one or more matches utilizing the tree ofoperations and the operators to generate search result output, each ofthe operators corresponding to a combination operation for the matches,wherein the combination operation operators include at least one of:“and” where a weight of the one or more matches is a product of inputweights; “or” where the weight of the one or more matches is a sum ofinput weights; “then” where the weight of the one or more matches is theproduct of input weights, and time ordering is enforced; “later” wherethe weight of the one or more matches is the product of input weights,and time ordering is enforced; “near” where the weight of the one ormore matches is the product of input weights when the one or morematches are found near each other a less than time operator where theweight of the one or more matches is the product of input weights when atime less than that specified separates the two matches; and a greaterthan time operator where the weight of the one or more matches is theproduct of input weights when a time more than that specified separatesthe two matches; returning the search result output after performingeach binary or unary operation on the one or more matches associatedwith the literals comprising the tree of operations; and reducing anumber of documents in the transcript data source by applying the searchresult output of the previous steps, thereby reducing a memory andcomputational overhead of a subsequent query.
 2. The method of claim 1,wherein determining the cross-correlation of the query matrix and themetadata-enriched text in the transcript matrix comprises: transformingthe query matrix and the transcript matrix into a query word embeddingmatrix and transcript word embedding matrix utilizing amulti-dimensional word embedding for each word in the query matrix andthe transcript matrix, wherein the query word embedding matrix iscreated by replacing each word in the query matrix with themulti-dimensional word embedding for that word and the transcript wordembedding matrix is created by replacing each word in the transcriptmatrix with the multi-dimensional word embedding for that word; anddetermining a sum of dot products of the query word embedding matrix andthe transcript word embedding matrix.
 3. The method of claim 2, whereinquery word-transcript word pairs from the query word embedding matrixand the transcript word embedding matrix and the sum of the dot productsfor each of the query word-transcript word pairs is stored in aquantitative thesaurus matrix.
 4. The method of claim 1, whereindetermining the cross-correlation of the query matrix and themetadata-enriched text in the transcript matrix comprises: transformingthe query matrix and the transcript matrix into a query word embeddingmatrix and a transcript word embedding matrix utilizing amulti-dimensional word embedding for each word in the query matrix andthe transcript matrix, wherein the query word embedding matrix iscreated by replacing each word in the query matrix with themulti-dimensional word embedding for that word and the transcript wordembedding matrix is created by replacing each word in the transcriptmatrix with the multi-dimensional word embedding for that word; applyinga fast Fourier transform to the transcript word embedding matrix and thequery word embedding matrix to generate a transformed transcript wordembedding matrix and a transformed query word embedding matrix;determining a point-wise product of the transformed transcript wordembedding matrix and the transformed query word embedding matrix; andapplying an inverse fast Fourier transform to the point-wise product torecover the cross-correlation of the transformed transcript wordembedding matrix with the transformed query word embedding matrix. 5.The method of claim 1, further comprising: transforming the query matrixand the transcript matrix into a query word embedding matrix and atranscript word embedding matrix utilizing a multi-dimensional wordembedding for each word in the query matrix and the transcript matrix,wherein the query word embedding matrix is created by replacing eachword in the query matrix with the multi-dimensional word embedding forthat word and the transcript word embedding matrix is created byreplacing each word in the transcript matrix with the multi-dimensionalword embedding for that word; determining when at least one of theliterals comprises two or more words; and on condition when at least oneof the literals comprises the two or more words, applying a Gaussiankernel to the transcript word embedding matrix before determining thecross-correlation using a fast Fourier transformation system.
 6. Themethod of claim 1, wherein determining the cross-correlation of thequery matrix and the metadata-enriched text in the transcript matrixcomprises: determining query word-transcript word pairs, wherein sums ofdot products for each of the query word-transcript word pairs are storedin a sparse matrix and are retrievable from the sparse matrix, whereinthe sparse matrix is a quantitative thesaurus matrix; sending a controlto the quantitative thesaurus matrix to return the similarity score foreach of the query word-transcript word pairs; and setting thecross-correlation equal to the similarity score.
 7. The method of claim1, further comprising: determining query word-transcript word pairs,wherein the sum of dot products for each of the query word-transcriptword pairs are stored in a sparse matrix and are retrievable from thesparse matrix, wherein the sparse matrix is a quantitative thesaurusmatrix; sending a control to the quantitative thesaurus matrix to returnsimilar words, the similar words having a similarity score above thethresholding softness for each of the literals; determining a set ofremaining documents of the transcript data source having at least one ofthe similar words; and utilizing the set of remaining documents as thetranscript data source.
 8. The method of claim 1, wherein the searchengine query further comprises non-speech information, furthercomprising adding a non-speech information dimension to the query matrixand the transcript matrix.
 9. The method of claim 8, wherein thenon-speech information is an emotion model: the non-speech informationdimension of the query matrix being a query flag; and the non-speechinformation dimension of the transcript matrix being an output of theemotion model.
 10. The method of claim 8, wherein the non-speechinformation is speaker metadata: the non-speech information dimension ofthe query matrix being a query flag; and the non-speech informationdimension of the transcript matrix being an indication of a speaker. 11.A computing apparatus, the computing apparatus comprising: a processor;and a memory storing instructions that, when executed by the processor,configure the apparatus to: parse a search engine query into literalsand operators, wherein each of the literals comprises at least one wordand wherein each of the operators is one of a binary operator and aunary operator, and the operators include at least one of phraseoperators, conversation operators, and time operators; generate a querymatrix from the literals; generate a transcript matrix from a documentof a transcript data source, wherein the transcript data sourcecomprises multiple documents storing metadata-enriched text generatedfrom recorded or streamed audio communications, wherein themetadata-enriched text includes word embeddings and embedded non-speechdata, wherein the word embeddings represent individual words usingreal-valued vectors in a predefined vector space and wherein words thathave a same meaning have a similarity score indicating related vectorrepresentations in the predefined vector space, and wherein thesimilarity score is determined by the cross-correlation of thereal-valued vectors representing the words in the predefined vectorspace, wherein the embedded non-speech data stores non-speechinformation detected in the recorded or streamed audio communications,and wherein the non-speech information includes at least one of:information regarding the meaning of words in the metadata-enrichedtext, a speaker role of a speaker in the recorded or streamed audiocommunications, and an emotion detected for the speaker from therecorded or streamed audio communication; determine a cross-correlationof the query matrix and the metadata-enriched text in the transcriptmatrix; apply a softness map to the cross-correlation to determine oneor more matches for each of the literals, the softness map generatedfrom a thresholding softness assigned to each of the literals as part ofthe search engine query, wherein the thresholding softness assigned toeach of the literals determines how much semantic difference ispermitted between the literal and the one or more matches for thatliteral, and wherein the one or more matches are identified from thecross-correlations that fall within the thresholding softness assignedto the literal; build a tree of operations, the tree of operationscomprising the literals and the operators of the search engine query;combine the one or more matches utilizing the tree of operations and theoperators to generate search result output, each of the operatorscorresponding to a combination operation for the matches, wherein thecombination operation operators include at least one of: “and” where aweight of the one or more matches is a product of input weights; “or”where the weight of the one or more matches is a sum of input weights;“then” where the weight of the one or more matches is the product ofinput weights, and time ordering is enforced; “later” where the wei htof the one or more matches is the roduct of in ut weights, and timeordering is enforced; “near” where the weight of the one or more matchesis the product of in u weights when the one or more matches are foundnear each other, a less than time operator where the weight of the oneor more matches is the product of input weights when a time less thanthat specified separates the two matches; and a reater than time oerator where the wei ht of the one or more matches is the product ofinput weights when a time more than that specified separates the twomatches; return the search result output after performing each binary orunary operation on the one or more matches associated with the literalscomprising the tree of operations; and reduce a number of documents inthe transcript data source by applying the search result output of theprevious steps, thereby reducing a memory and computational overhead ofa subsequent query.
 12. The computing apparatus of claim 11, whereindetermining the cross-correlation of the query matrix and themetadata-enriched text in the transcript matrix comprises: transform thequery matrix and the transcript matrix into a query word embeddingmatrix and a transcript word embedding matrix utilizing amulti-dimensional word embedding for each word in the query matrix andthe transcript matrix, wherein the query word embedding matrix iscreated by replacing each word in the query matrix with themulti-dimensional word embedding for that word and the transcript wordembedding matrix is created by replacing each word in the transcriptmatrix with the multi-dimensional word embedding for that word; anddetermine a sum of dot products of the query word embedding matrix andthe transcript word embedding matrix.
 13. The computing apparatus ofclaim 12, wherein query word-transcript word pairs from the query wordembedding matrix and the transcript word embedding matrix and the sum ofthe dot products for each of the query word-transcript word pairs isstored in a quantitative thesaurus matrix.
 14. The computing apparatusof claim 11, wherein determining the cross-correlation of the querymatrix and the metadata-enriched text in the transcript matrixcomprises: transform the query matrix and the transcript matrix into aquery word embedding matrix and a transcript word embedding matrixutilizing a multi-dimensional word embedding for each word in the querymatrix and the transcript matrix, wherein the query word embeddingmatrix is created by replacing each word in the query matrix with themulti-dimensional word embedding for that word and the transcript wordembedding matrix is created by replacing each word in the transcriptmatrix with the multi-dimensional word embedding for that word; apply afast Fourier transform to the transcript word embedding matrix and thequery word embedding matrix to generate a transformed transcript wordembedding matrix and a transformed query word embedding matrix;determine a point-wise product of the transformed transcript wordembedding matrix and the transformed query word embedding matrix; andapply an inverse fast Fourier transform to the point-wise product torecover the cross-correlation of the transformed transcript wordembedding matrix with the transformed query word embedding matrix. 15.The computing apparatus of claim 11, wherein the instructions furtherconfigure the apparatus to: transform the query matrix and thetranscript matrix into a query word embedding matrix and a transcriptword embedding matrix utilizing a multi-dimensional word embedding foreach word in the query matrix and the transcript matrix, wherein thequery word embedding matrix is created by replacing each word in thequery matrix with the multi-dimensional word embedding for that word andthe transcript word embedding matrix is created by replacing each wordin the transcript matrix with the multi-dimensional word embedding forthat word; determine when at least one of the literals comprises two ormore words; and on condition when at least one of the literals comprisesthe two or more words, apply a Gaussian kernel to the transcript wordembedding matrix before determining the cross-correlation using a fastFourier transformation system.
 16. The computing apparatus of claim 11,wherein determining the cross-correlation of the query matrix and themetadata-enriched text in the transcript matrix comprises: determinequery word-transcript word pairs, wherein sums of dot products for eachof the query word-transcript word pairs are stored in a sparse matrixand are retrievable from the sparse matrix, wherein the sparse matrix isa quantitative thesaurus matrix; send a control to the quantitativethesaurus matrix to return the similarity score for each of the queryword-transcript word pairs; and set the cross-correlation equal to thesimilarity score.
 17. The computing apparatus of claim 11, wherein theinstructions further configure the apparatus to: determine queryword-transcript word pairs, wherein the sum of dot products for each ofthe query word-transcript word pairs are stored in a sparse matrix andare retrievable from the sparse matrix, wherein the sparse matrix is aquantitative thesaurus matrix; send a control to the quantitativethesaurus matrix to return similar words, the similar words having asimilarity score above the thresholding softness for each of theliterals; determine a set of remaining documents of the transcript datasource having at least one of the similar words; and utilize the set ofremaining documents as the transcript data source.
 18. The computingapparatus of claim 11, wherein the search engine query further comprisesnon-speech information, wherein the instructions further configure theapparatus to add a non-speech information dimension to the query matrixand the transcript matrix.
 19. The computing apparatus of claim 18,wherein the non-speech information is an emotion model: the non-speechinformation dimension of the query matrix being a query flag; and thenon-speech information dimension of the transcript matrix being anoutput of the emotion model.
 20. The computing apparatus of claim 18,wherein the non-speech information is speaker metadata: the non-speechinformation dimension of the query matrix being a query flag; and thenon-speech information dimension of the transcript matrix being anindication of a speaker.